Semi-hierarchical based motion estimation algorithm for the dirac video encoder

Having fast and efficient motion estimation is crucial in today’s advance video compression technique since it determines the compression efficiency and the complexity of a video encoder. In this paper, a method which we call semi-hierarchical motion estimation is proposed for the Dirac video encoder. By considering the fully hierarchical motion estimation only for a certain type of inter frame encoding, complexity of the motion estimation can be greatly reduced while maintaining the desirable accuracy. The experimental results show that the proposed algorithm gives two to three times reduction in terms of the number of SAD calculation compared with existing motion estimation algorithm of Dirac for the same motion estimation accuracy, compression efficiency and PSNR performance. Moreover, depending upon the complexity of the test sequence, the proposed algorithm has the ability to increase or decrease the search range in order to maintain the accuracy of the motion estimation to a certain level

Evolutionary strategy search algorithm for fast block motion estimation

The evolutionary strategy search (ESS) algorithm is a novel method for implementing fast block motion estimation (ME) using evolutionary strategy (ES). ESS uses a combination of ideas based on existing search strategies and employs a novel (1þsl) ES implementation. It is essentially a succession of random searches, but by controlling the placement and distribution of these searches in a simple way, it proves possible to achieve comparable motion vector accuracy to the more established ME strategies, but with enhanced convergence speed

A Three-Point Directional Search Block Matching Algorithm

This paper proposes compact directional asymmetric search patterns, which we have named as three-point directional search (TDS). In most fast search motion estimation algorithms, a symmetric search pattern is usually set at the minimum block distortion point at each step of the search. The design of the symmetrical pattern in these algorithms relies primarily on the assumption that the direction of convergence is equally alike in each direction with respect to the search center. Therefore, the monotonic property of real-world video sequences is not properly used by these algorithms. The strategy of TDS is to keep searching for the minimum block distortion point in the most probable directions, unlike the previous fast search motion estimation algorithms where all the directions are checked. Therefore, the proposed method significantly reduces the number of search points for locating a motion vector. Compared to conventional fast algorithms, the proposed method has the fastest search speed and most satisfactory PSNR values for all test sequences

Fast motion estimation algorithm in H.264 standard

In H.264/AVC standard, the block motion estimation pattern is used to estimate the motion which is a very time consuming part. Although many fast algorithms have been proposed to reduce the huge calculation, the motion estimation time still cannot achieve the critical real time application. So to develop an algorithm which will be fast and having low complexity became a challenge in this standard.For this reasons, a lot of block motion estimation algorithms have been proposed. Typically the block motion estimation part is categorized into two parts. (1) Single pixel motion estimation (2) Fractional pixel motion estimation. In single pixel motion estimation one kind of fast motion algorithm uses fixed pattern like Three Step search, 2-D Logarithmic Search. Four Step search,Diamond Search, Hexagon Based Search. These algorithms are able to reduce the search point and get good coding quality. But the coding quality decreases when the fixed pattern does not fit the real life video sequence. In this thesis we tried to reduce the time complexity and number of search point by using an early termination method which is called adaptive threshold selection. We have used this method in three step search (TSS) and four step search and compared the performance with already existing block matching algorithm.This thesis work proposes fast sub-pixel motion estimation techniques having lower computational complexity. The proposed methods are based on mathematical models of the motion compensated prediction errors in compressing moving pictures. Unlike conventional hierarchical motion estimation techniques, the proposed methods avoid sub-pixel interpolation and subsequent secondary search after the integer-precision motion estimation, resulting in reduced computational time. In order to decide the coefficients of the models, the motion-compensated prediction errors of the neighboring pixels around the integer-pixel motion vector are utilized

New Fast Search Algorithm for Base Layer of H.264 Scalable Video Coding Extension

In this contribution, a fast search motion estimation algorithm for H.264/AVC SVC (scalable video coding) [2] base layer with hierarchical B-frame structure for temporal decomposition is presented and compared with fast search motion estimation algorithm in JSVM software [1], that is the reference software for H.264/AVC SVC. The proposed technique is a block-matching based motion estimation algorithm working in two steps, called Coarse search and Fine search. The Coarse search is performed for each frame in display order, and for each 16x16 macroblock chooses the best motion vector at half pel accuracy. Fine search is performed for each frame in encoding order and finds the best prediction for each block type, reference frame and direction, choosing the best motion vector at quarter pel accuracy using R-D optimization. Both Coarse and Fine Search test 3 spatial and 3 temporal predictors, and add to the best one a set of updates. The spatial predictors for the fine search are the result of the Fine search already performed for the previous blocks, while the temporal predictors are the results of Coarse Search scaled by an appropriate coefficient. This scaling is performed since in the Coarse search each picture is always estimated with respect to the previous one, while in the Fine Search the temporal distance between the current picture and its references depend on the temporaldecomposition level. Moreover in Fine search the number and the value of the updates tested depend on the distance between the current picture and its references. These sets of updates are the result of a huge number of simulations on test sequences with different motion features. The proposed algorithm has been tested on the set of test sequences proposed by JVT group, using different resolutions and temporal decomposition structures. The proposed method can reduce the average coding complexity in terms of motion vector tested from 70 to 90 percent with respect to the Fast-ME JVT method, while the quality loss depends on the GOP dimension, that is the most critical parameter for the performance of the algorithm. In fact for small GOP dimensions (4 or 8) the algorithm has the same quality at equal bit-rate respect to the Fast-ME JVT method for almost all the sequences and better quality for some sequences. For medium and long GOP dimensions (16-32) the algorithm has a quality loss lower than 0.5 dB for all the tested sequences

Optimization of the motion estimation for parallel embedded systems in the context of new video standards

15 pagesInternational audienceThe effciency of video compression methods mainly depends on the motion compensation stage, and the design of effcient motion estimation techniques is still an important issue. An highly accurate motion estimation can significantly reduce the bit-rate, but involves a high computational complexity. This is particularly true for new generations of video compression standards, MPEG AVC and HEVC, which involves techniques such as different reference frames, sub-pixel estimation, variable block sizes. In this context, the design of fast motion estimation solutions is necessary, and can concerned two linked aspects: a high quality algorithm and its effcient implementation. This paper summarizes our main contributions in this domain. In particular, we first present the HME (Hierarchical Motion Estimation) technique. It is based on a multi-level refinement process where the motion estimation vectors are first estimated on a sub-sampled image. The multi-levels decomposition provides robust predictions and is particularly suited for variable block sizes motion estimations. The HME method has been integrated in a AVC encoder, and we propose a parallel implementation of this technique, with the motion estimation at pixel level performed by a DSP processor, and the sub-pixel refinement realized in an FPGA. The second technique that we present is called HDS for Hierarchical Diamond Search. It combines the multi-level refinement of HME, with a fast search at pixel-accuracy inspired by the EPZS method. This paper also presents its parallel implementation onto a multi-DSP platform and the its use in the HEVC context

Semi-hierarchical based motion estimation algorithm for the dirac video encoder

Having fast and efficient motion estimation is crucial in today’s advance video compression technique since it determines the compression efficiency and the complexity of a video encoder. In this paper, a method which we call semi-hierarchical motion estimation is proposed for the Dirac video encoder. By considering the fully hierarchical motion estimation only for a certain type of inter frame encoding, complexity of the motion estimation can be greatly reduced while maintaining the desirable accuracy. The experimental results show that the proposed algorithm gives two to three times reduction in terms of the number of SAD calculation compared with existing motion estimation algorithm of Dirac for the same motion estimation accuracy, compression efficiency and PSNR performance. Moreover, depending upon the complexity of the test sequence, the proposed algorithm has the ability to increase or decrease the search range in order to maintain the accuracy of the motion estimation to a certain level

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

Block matching algorithm based on Harmony Search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate.Comment: 25 Pages. arXiv admin note: substantial text overlap with arXiv:1405.472